Anytime Bottom-Up Rule Learning for Knowledge Graph Completion
Christian Meilicke, Melisachew Wudage Chekol, Daniel Ruffinelli, Heiner Stuckenschmidt · 2019
We propose an anytime bottom-up technique for learning logical rules from large knowledge graphs. We apply the learned rules to predict candidates in the context of knowledge graph completion. Our approach outperforms other rule-based approaches and it is competitive with current state of the art, which is based on latent representations. Besides, our approach is significantly faster, requires less computational resources, and yields an explanation in terms of the rules that propose a candidate.